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AI+ Context Engineering Practitioner™
USD$195.00 (GST excl.)
Formerly known as AI+ Context Engineering™
Master AI+ Context Engineering Practitioner™ for Production-Grade AI Systems
- Context Strategy & Architecture: Learn how to design robust context architectures that go beyond prompts—managing instructions, memory, tools, and knowledge for reliable AI behavior across sessions and workflows.
- Building Context-Aware AI Systems: Gain hands-on skills in implementing context pipelines, RAG architecture, and memory systems that ensure grounded, accurate, and cost-efficient AI outputs.
- Context Management & Optimization: Master the Write-Select-Compress-Isolate (W-S-C-I) framework to control relevance, reduce hallucinations, optimize token usage, and scale AI systems effectively.
- Enterprise-Grade Context Integration: Learn how to integrate AI safely into enterprise environments with role-based access, compliance guardrails, secure memory, and conflict-free context orchestration.
- Future-Ready Agent & Workflow Design: Prepare for the next wave of AI by designing multi-agent systems, automated workflows, and context-driven architectures that remain reliable as models, tools, and scale evolve.
Duration
Instructor-Led: 1 day (live or virtual)
Self-Paced: 8 hours of content
Self-Paced: 8 hours of content
Format
Online, self-paced
Exam
50 questions, 70% passing, 90 minutes, online proctored exam
Overview
- Context Strategy & Architecture: Learn how to design robust context architectures that go beyond prompts—managing instructions, memory, tools, and knowledge for reliable AI behavior across sessions and workflows.
- Building Context-Aware AI Systems: Gain hands-on skills in implementing context pipelines, RAG architecture, and memory systems that ensure grounded, accurate, and cost-efficient AI outputs.
- Context Management & Optimization: Master the Write-Select-Compress-Isolate (W-S-C-I) framework to control relevance, reduce hallucinations, optimize token usage, and scale AI systems effectively.
- Enterprise-Grade Context Integration: Learn how to integrate AI safely into enterprise environments with role-based access, compliance guardrails, secure memory, and conflict-free context orchestration.
- Future-Ready Agent & Workflow Design: Prepare for the next wave of AI by designing multi-agent systems, automated workflows, and context-driven architectures that remain reliable as models, tools, and scale evolve.
Prerequisites
A solid foundation in AI and machine learning concepts, proficiency in programming and data handling, familiarity with cloud platforms and IoT environments, and the ability to design, manage, and optimize contextual data, memory, and tool orchestration are essential for this course.
What you’ll learn
- AI Engineers & LLM Developers: Built for practitioners who want to move beyond basic prompt engineering and design production-grade, context-aware AI systems using RAG, memory, tools, and orchestration patterns
- Product Managers & AI Architects: Ideal for professionals responsible for shipping reliable AI features who need to understand context pipelines, grounding, cost control, and system-level design tradeoffs rather than toy demos
- Data & Platform Engineers: For engineers working with vector databases, embeddings, retrieval systems, and AI infrastructure who want to architect scalable, efficient, and trustworthy context flows
- Enterprise & Solution Architects: Designed for architects building AI systems in regulated or large-scale environments who must manage security, compliance, cost optimization, and multi-agent orchestration
- AI Consultants & Technical Leaders: For professionals advising organizations on AI adoption who need a deep, practical understanding of why context—not just models—is the real differentiator in modern AI systems
- Advanced No-Code / Automation Builders: A strong fit for builders using tools like n8n, Make, or Zapier who want to design reliable AI workflows and agentic systems without writing heavy infrastructure code
Why this certification matters
- Go beyond prompts Learn to engineer instructions, tools, memory, and state so AI behaves reliably.
- Production-ready systems Build RAG + context pipelines that reduce hallucinations and improve grounding.
- Scale with efficiency Master selection + compression to control token cost, latency, and performance.
- Enterprise-safe AI Apply PII controls, role-based filtering, and conflict resolution for compliant deployments.
- Real deliverable Complete a multi-agent capstone (n8n) with routing + calculations + policy RAG.
Tools you’ll use
LangChain and LangGraph
LlamaIndex
Vector Databases (Pinecone, Chroma)
n8n, Zapier, Make.com
Embedding Models and RAG Pipelines
No-Code Automation Platforms
Enterprise Data and API Integrations
Learning Modules
8 modules
1Module 1: Foundations of Context Engineering – Introduction
1.1 What is Context Engineering (Beyond Prompt Engineering)
1.2 From Prompting to Context Pipelines: The 2025 Paradigm Shift
1.3 The Four Building Blocks of Context: Instructions, Knowledge, Tools, State
1.4 Short-Term vs Long-Term Memory in LLM Systems
1.5 Benefits of Context Engineering: Grounding, Relevance, Continuity, Cost Control
1.6 Use Case: Context-Aware AI Travel Assistant
1.7 Hands-on: Designing System Instructions and Memory State for a Role-Based AI Agent
1.2 From Prompting to Context Pipelines: The 2025 Paradigm Shift
1.3 The Four Building Blocks of Context: Instructions, Knowledge, Tools, State
1.4 Short-Term vs Long-Term Memory in LLM Systems
1.5 Benefits of Context Engineering: Grounding, Relevance, Continuity, Cost Control
1.6 Use Case: Context-Aware AI Travel Assistant
1.7 Hands-on: Designing System Instructions and Memory State for a Role-Based AI Agent
2Module 2: Context Management Patterns & Techniques
2.1 The W-S-C-I Framework: Write, Select, Compress, Isolate
2.2 WRITE Strategy: Agent Identity, Persona, Guardrails, and State
2.3 SELECT Strategy: Precision Retrieval & Metadata Filtering
2.4 COMPRESS Strategy: Summarization, Token Optimization, Auto-Compaction
2.5 ISOLATE Strategy: Context Boundaries, Safety, and Focus
2.6 Advanced Retrieval Patterns: Hybrid Search, Semantic Chunking
2.7 Case Study: ChatGPT & Claude Memory Systems
2.8 Hands-on: Implement Context Selection & Compression Using LangChain / LlamaIndex
2.2 WRITE Strategy: Agent Identity, Persona, Guardrails, and State
2.3 SELECT Strategy: Precision Retrieval & Metadata Filtering
2.4 COMPRESS Strategy: Summarization, Token Optimization, Auto-Compaction
2.5 ISOLATE Strategy: Context Boundaries, Safety, and Focus
2.6 Advanced Retrieval Patterns: Hybrid Search, Semantic Chunking
2.7 Case Study: ChatGPT & Claude Memory Systems
2.8 Hands-on: Implement Context Selection & Compression Using LangChain / LlamaIndex
3Module 3: Context Pipelines, RAG & Grounding Architecture
3.1 The End-to-End Context Pipeline (Input → Retrieval → Compression → Assembly → Response → Update)
3.2 Retrieval-Augmented Generation (RAG) Architecture Deep Dive
3.3 Vector Databases: Pinecone, Chroma & Embedding Models
3.4 Grounding Failures: Hallucinations, Context Poisoning, Distraction
3.5 Mitigation Techniques: Rerankers, Provenance, Context Forensics
3.6 Case Study: Anthropic’s Multi-Agent Researcher (MAR)
3.7 Hands-on: Build a RAG Pipeline with Vector Search and Grounded Responses
3.2 Retrieval-Augmented Generation (RAG) Architecture Deep Dive
3.3 Vector Databases: Pinecone, Chroma & Embedding Models
3.4 Grounding Failures: Hallucinations, Context Poisoning, Distraction
3.5 Mitigation Techniques: Rerankers, Provenance, Context Forensics
3.6 Case Study: Anthropic’s Multi-Agent Researcher (MAR)
3.7 Hands-on: Build a RAG Pipeline with Vector Search and Grounded Responses
4Module 4: Optimization, Scaling & Enterprise Readiness
4.1 Token Economy & Cost Optimization in Context Pipelines
4.2 Context Scaling & the Model Context Protocol (MCP)
4.3 Security & Compliance: PII Filtering, Redaction, Role-Based Access
4.4 Conflict Resolution & Context Consistency
4.5 Multi-Modal Context: Text, Tables, PDFs, Video Transcripts
4.6 Case Studies: Walmart “Ask Sam” & Morgan Stanley Knowledge Assistant
4.7 Hands-on: Implement Role-Based Context Filtering and Secure Retrieval
4.2 Context Scaling & the Model Context Protocol (MCP)
4.3 Security & Compliance: PII Filtering, Redaction, Role-Based Access
4.4 Conflict Resolution & Context Consistency
4.5 Multi-Modal Context: Text, Tables, PDFs, Video Transcripts
4.6 Case Studies: Walmart “Ask Sam” & Morgan Stanley Knowledge Assistant
4.7 Hands-on: Implement Role-Based Context Filtering and Secure Retrieval
5Module 5: Context Flow Design for Business Users (No-Code AI)
5.1 Translating Business Processes into AI-Ready Context Flows
5.2 Context Flow Diagrams (CFDs) & Automated Workflow Architecture (AWA)
5.3 Implementing W-S-C-I Visually Using No-Code Tools (n8n / Make / Zapier)
5.4 Context Templates for Consistency & Structured Outputs
5.5 Use Case: Dynamic Customer Onboarding Assistant
5.6 Case Studies: Airbnb Support Automation & HSBC SME Lending
5.7 Hands-on: Build a Context Flow Using No-Code Orchestration
5.2 Context Flow Diagrams (CFDs) & Automated Workflow Architecture (AWA)
5.3 Implementing W-S-C-I Visually Using No-Code Tools (n8n / Make / Zapier)
5.4 Context Templates for Consistency & Structured Outputs
5.5 Use Case: Dynamic Customer Onboarding Assistant
5.6 Case Studies: Airbnb Support Automation & HSBC SME Lending
5.7 Hands-on: Build a Context Flow Using No-Code Orchestration
6Module 6: Real-World Industry Context Applications
6.1 Context Engineering in Regulated Domains
6.2 Healthcare: Clinical Decision Support & PHI Isolation
6.3 Finance: Market Analysis, Compliance Summarization & Tool-Based Context
6.4 Legal & Education: Precision Retrieval & Personalized Learning Context
6.5 Risk Mitigation: Context Poisoning & Context Clash
6.6 Advanced Agent Memory for Long-Horizon Tasks
6.7 Case Studies: Activeloop (Legal/IP) & Five Sigma (Insurance)
6.2 Healthcare: Clinical Decision Support & PHI Isolation
6.3 Finance: Market Analysis, Compliance Summarization & Tool-Based Context
6.4 Legal & Education: Precision Retrieval & Personalized Learning Context
6.5 Risk Mitigation: Context Poisoning & Context Clash
6.6 Advanced Agent Memory for Long-Horizon Tasks
6.7 Case Studies: Activeloop (Legal/IP) & Five Sigma (Insurance)
7Module 7: Multi-Agent Orchestration & the Future
7.1 Why Monolithic Agents Fail: Context Explosion
7.2 Multi-Agent Systems (MAS) & Context Isolation
7.3 Agent Roles: Router, Planner, Executor
7.4 Agent-to-Agent Context Compression
7.5 Guardrails, Governance & Inter-Agent Safety
7.6 Ethics, Bias Mitigation & Source Traceability
7.7 Case Studies: IBM Watson Orchestrate & Enterprise Context Orchestrators
7.8 Career Pathways: Context Architect & AI Governance Roles
7.2 Multi-Agent Systems (MAS) & Context Isolation
7.3 Agent Roles: Router, Planner, Executor
7.4 Agent-to-Agent Context Compression
7.5 Guardrails, Governance & Inter-Agent Safety
7.6 Ethics, Bias Mitigation & Source Traceability
7.7 Case Studies: IBM Watson Orchestrate & Enterprise Context Orchestrators
7.8 Career Pathways: Context Architect & AI Governance Roles
8Module 8: Capstone Project & Certification
8.1 Capstone Overview: Multi-Agent Context-Aware System
8.2 Build: Query Router with Financial Calculations & Policy RAG (n8n)
8.3 Presentation, Review & Feedback
8.4 Final Evaluation & AI+ Context Engineering Practitioner™ Certification
8.2 Build: Query Router with Financial Calculations & Policy RAG (n8n)
8.3 Presentation, Review & Feedback
8.4 Final Evaluation & AI+ Context Engineering Practitioner™ Certification
| Format | Online, self-paced |
|---|---|
| Duration | Instructor-Led: 1 day (live or virtual) |
| Exam | 50 questions, 70% passing, 90 minutes, online proctored exam |
| Included | Instructor-led OR Self-paced course + Official exam + Digital badge |
| Prerequisites | A solid foundation in AI and machine learning concepts, proficiency in programming and data handling, familiarity with cloud platforms and IoT environments, and the ability to design, manage, and optimize contextual data, memory, and tool orchestration are essential for this course. |
| Delivery | Projects & case studies |
| Outcome | Industry-recognized credential + hands-on experience |
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